On the other hand, we flag 52 “unbroken" CA patterns as potential false negatives in Figure REF (f). These fall into two groups: (1) patterns that require more than 25 \(X^\text{d}\)  clusters to be removed in Step 4 before computing curviness, or (2) patterns with at least one stripe–interstripe boundary with curviness greater than 25. Patterns meeting these conditions are rare, as indicated by the distributions in Figures REF (c)–(d). We show a few example CA patterns among these 52 potential false negatives in Figures REF (e)–(g): these patterns clearly include large \(X^\text{d}\)  spots or tortuous boundaries. Because our methods do not directly quantify spots, flagging CA patterns in Figure REF (f) with very high curviness or many outlier \(X^\text{d}\)  cells serves as a rough proxy for indicating that a subset of “unbroken" patterns feature spot imperfections. Adding on to our methods and those in [1]} to identify and characterize spots in CA patterns directly is an important direction for future research.
